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Network Coding-Based Socially-Aware Caching Strategy in D2D

机译：基于网络编码的D2D中的社会感知缓存策略

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摘要

By caching the most popular content into mobile devices, users can retrieve content directly from nearby devices through Device to Device (D2D) communications, which can significantly reduce backhaul traffic and improve network performance. Most existing D2D cache placement strategies are proactive approaches, which cannot deal with the problem of timely cache updating. In this paper, we propose a network coding-based socially-aware D2D caching strategy, which takes geographical proximity and the social relationships of users into consideration. First, a physical D2D network with high communication reliability is built according to the geographical proximity, composed of devices with high probability of communicating to each other through stable D2D communications. According to the social relationship between users within the physical D2D network, we partition the devices into communities and rank the devices within same community by their influence degree. Within a community, each caching decision is made independently according to the user-content contribution degree. To calculate the contribution degree, the impact between devices on requesting same content is modeled using an Indian Buffet Process. Devices cache coded blocks instead of the whole content to improve caching efficiency. Simulation results show that the proposed strategy achieves higher cache hit and sum rates compared to other schemes.
机译：通过将最流行的内容缓存到移动设备中，用户可以通过设备直接从附近设备通过设备检索内容（D2D）通信，这可以显着降低回程流量并提高网络性能。大多数现有D2D缓存放置策略是主动方法，无法处理及时缓存更新的问题。在本文中，我们提出了一种基于网络编码的社会感知D2D缓存策略，其考虑了地理接近和用户的社交关系。首先，根据地理接近度建立具有高通信可靠性的物理D2D网络，由具有通过稳定D2D通信彼此通信的高概率的设备构成。根据物理D2D网络中的用户之间的社交关系，我们将设备分区为社区，并通过其影响程度将设备中的设备排列。在一个社区内，每个缓存决定根据用户内容贡献程度独立完成。为了计算贡献程度，使用印度自助餐进程建模了对请求相同内容的设备之间的影响。设备缓存编码块代替整个内容以提高缓存效率。仿真结果表明，与其他方案相比，该拟议策略达到了更高的缓存命中和总和率。

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